Dental imaging using segmentation and an arch

ABSTRACT

A method and a system for generating an image by obtaining x-ray image data, segmenting the x-ray image data into a first portion above a vertical threshold and a second portion below the vertical threshold. Further, the method and the system include generating an arch for the second plurality of slices, and generating an image based on the arch.

RELATED APPLICATIONS

The present patent application is a continuation of U.S. applicationSer. No. 12/847,590, filed on Jul. 30, 2010, which claims priority toU.S. Provisional Application No. 61/230,411, filed on Jul. 31, 2009, thecontent of which is hereby incorporated by reference.

BACKGROUND

The present invention relates to x-ray imaging. More particularly,embodiments of the invention relate to panoramic imaging of the humanmouth and similar structures.

X-rays have been used in dentistry to image teeth and parts of the mouthfor many years. In general, the process involves generating x-rays anddirecting the x-rays at the patient's mouth. The x-rays are absorbed andreflected differently by different parts of the mouth (e.g., bone versustissue). This difference in absorption is used to create an image, suchas on film or by using an electronic image sensor. Individual images ofspecific areas of interest can be generated or, if a wider perspectiveis desired, a panoramic image can be created. Often, a computertomography (“CT”) system is used to generate a panoramic image. In atypical dental CT system, the patient sits upright, and the x-ray sourceand detector are mounted on opposite ends of a gantry that rotates abouta vertical axis through the middle of the patient's head. In general, apanoramic image of the jaw depicts the jaw as if it were imaged onto acylindrical sheet with the axis of the sheet upright, and as if thesheet were then unrolled into a flat form.

SUMMARY

Although a number of technologies designed to generate panoramic imagesof the mouth exist, there are a number of deficiencies with thesetechnologies. For example, since the jaw is not cylindrical, certaindistortions and inaccuracies occur in a panoramic image created usingmany known technologies. In addition, image quality in many currentlyavailable systems is less than desired because, among other things, thepanoramic images fail to provide an image in which the anterior frontteeth, side teeth, sinus floor, and nerve canal appear clearly and in amanner that closely resembles the actual anatomy of the individual ofwhom the image is being taken. For example, some prior panoramic systemsproduce images with concaved front and hidden mandibular condyles.

Embodiments of the invention provide, among other things, a four-stepprocess is used to generate an image having a more realistic depictionof actual anatomy than at least some prior-art devices. The first partof the process involves segmentation and visualization of the jaw (orbone) from other parts (flesh, gums, and the like) of the image. Thefirst part of the process uses a “region growing algorithm.” In a secondpart of the process, a detection of the jaw arch is carried out byseparating image data into slices and applying a curve-fittingtechnique. In a third part of the process, information regarding the jawarch is used to detect a master arch. Finally, in a fourth part of theprocess, a panoramic image is generated using the master arch andrendering geometry.

The invention also provides a method for generating a panoramic x-rayimage. The method includes obtaining, with an x-ray detector, volumetricx-ray image data having a first plurality of slices, segmenting, with acomputer, the x-ray image data into a first portion above a verticalthreshold and a second portion below the vertical threshold, andseparating the second portion into a second plurality of slices. Themethod further includes generating a plurality of curves for each slicein the second plurality of slices, generating a master arch for thesecond plurality of slices, and generating a panoramic image based onthe master arch.

In addition, the invention provides a panoramic x-ray system withenhanced image quality. The system comprises a gantry, an x-ray sourcemounted on the gantry, an x-ray-detector mounted opposite the x-raysource on the gantry, and a computer that receives volumetric image datafrom the x-ray detector. The computer segments the image data into afirst portion above a vertical threshold and a second portion below thevertical threshold, separates the second portion of data into aplurality of slices, generates a plurality of curves for each slice ofthe plurality of slices, generates a master arch for the plurality ofslices, and generates a panoramic image based on the master arch.

The invention further provides a method of generating jaw image data.The method comprises obtaining, with an x-ray detector, volumetric x-rayimage data including a plurality of slices, each slice having aplurality of voxel values. The method further comprises selecting, witha computer, a sagittal slice from the volumetric image data, anditeratively checking, with a computer, voxel values in the sagittalslice. The method further comprises seeding each slice in the pluralityof slices, performing region growing, generating a set of images basedon the region growing, and generating a three-dimensional image based onthe set of images.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a dental x-ray system for generating apanoramic x-ray image.

FIG. 2 is a flow chart illustrating a jaw segmentation and visualizationprocess performed by the system shown in FIG. 1.

FIG. 3 is a flow chart illustrating an automatic jaw arch detectionprocess performed by the system shown in FIG. 1.

FIG. 4 is a flow chart illustrating a master arch detection processperformed by the system shown in FIG. 1.

FIG. 5 is a flow chart illustrating a panoramic image reconstructionprocess performed by the system shown in FIG. 1.

FIG. 6 is a flow chart illustrating the detail steps of the automaticjaw arch detection process of FIG. 2 in greater detail.

FIG. 7 is a flow chart illustrating the detail steps of the automaticjaw arch detection process of FIG. 3 in greater detail.

FIG. 8 is a flow chart illustrating the detail steps of the master archdetection process of FIG. 4 and the panoramic image reconstructionprocess of FIG. 5 in greater detail.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways.

Referring to the drawings, and initially to FIG. 1, one form of atomographic apparatus according to an embodiment of the invention,indicated generally by the reference numeral 10, includes a scanner 12and a computer 14. The computer 14 has various input and output devicessuch as a keyboard 16, a cursor-control device (e.g., a mouse) and amonitor or display 20. The scanner 12 includes a source of x-rays 22, anx-ray detector or sensor 26, and a chair or seat 28. In the embodimentshown, the scanner 12 is arranged to image the head, or part of thehead, of a human patient P, especially the jaws and teeth of thepatient. The scanner also includes a rest or restrainer 32 to supportthe patient's head and face. The x-ray source 22 and sensor 26 aremounted on a rotating carrier or gantry 34 so as to circle round thepatient's head, while remaining aligned with one another (opposite fromone another). When the x-ray source is activated, it generates a streamof x-rays. When a patient is properly positioned in the seat 28 andrestrainer 32, the x-rays (or at least some x-rays) pass through thepatient's head and the sensor 26 generates x-ray images of the patient'shead. Numerous images are generated as the source 22 and sensor 26rotate around the patient's head. The computer 14 receives the x-rayimage data from the scanner 12 and, as is discussed below, generates apanoramic image based on the captured image data.

As noted above, prior systems often produce panoramic images that aredistorted or inaccurate. One objective of the invention is to provide apanoramic image that more accurately depicts the entire jaw denturestructure, including the teeth, sinus floor, and mandibular condyles.Images generated using embodiments of the invention exhibit an anatomicstructure size in a ratio of 1:1 that is uniformly proportional to thepanoramic image from visualization and spatial-measurement perspectives.

FIGS. 2 through 5 show, in a general manner, an image generation processcarried out by embodiments of the invention. The process is performed bythe computer 14 based on image data, having plurality of slices,received from the scanner 12 (a volumetric x-ray image data set or aportion of a volumetric data set). Generally, the computer 14 isprogrammed with software designed to carry out the processes outlinedbelow. It should be noted that embodiments of the invention could alsobe implemented using application-specific hardware or a combination ofapplication-specific hardware and software running on programmabledevices. As shown in FIG. 2, the first overall step in the processincludes jaw segmentation and visualization. Image data 50 from thesensor 26 is processed by performing vertical separation (step 52) toseparate or divide the image data into two portions: data below avertical threshold and data above the vertical threshold. Divided data54 (which includes data related to both soft tissue and hard tissue(e.g., bone)) is then processed (step 56) to separate soft tissue fromhard tissue to produce jaw image data 58. As shown in FIG. 3, the jawimage data 58 is separated into slices 64 (step 62), and the slices 64are modified using curve-fitting (step 72). The curve fitting generatesthree curves for each slice: an outer curve 74, inner curve 76, andcentral curve 78. These steps are performed by an automatic jaw archdetection process. As shown in FIG. 4, the curves 74-78 are used in amaster arch detection process (step 85), which generates a master arch90. As shown in FIG. 5, the master arch 90 is used in a panoramic imagereconstruction process (step 96) to generate a panoramic image 100.Additional details of the process illustrated in FIGS. 2 through 5 areprovided below.

FIG. 6 provides additional information regarding the segmentation andvisualization process of FIG. 2. As shown in FIG. 6, image data isobtained (step 102) (i.e., the image data 50) and segmentation isachieved by detecting the hard palate in the patient's jaw (step 104).The purpose of hard palate detection is to separate the anatomy ofinterest to a dentist (or other medical professional) from other partsof the head. Typically, the areas of interest include the mandible,mandibular condyles, maxilla, teeth, teeth apices, nerve canal paths,and sinus floor. Except for the mandibular condyles and sinus floor, thetissue and parts of the head located above the hard palate are typicallynot of interest to a dentist.

To detect the hard palate, the middle sagittal slice of image data isselected. Iterative checking of voxels in this slice of data is thenused to determine the position of the hard palate (which is, of course,in general terms, an anatomical feature). The analysis of the data isimpacted by a number of conditions including the presence of metalartifacts in the subject's teeth (e.g., fillings in cavities, braces,etc.), the upper space fosse, the lower hyoid, and the curvature of thehard palate.

In particular, the interactive voxel checking process checks voxelvalues starting from the right half of the middle sagittal slice. Thecheck begins at the bottom of the sagittal slice and moves upward (inthe vertical direction) and determines whether the voxel intensityvalues correspond to values associated with the hard palate or otheranatomical features. A check is also performed to see if the valuescorrespond to possible metal artifacts.

Once a voxel is determined (or verified) to be on the hard palate(understanding that that the hard palate is not a simple horizontalplane), a vertical level located a predetermined distance (e.g., 5 mm)below the vertical level of the verified voxel is selected as theseparation level.

In a supplemental step, if the calculated value of the hard palatedeviates from the average empirical value of the height of hard palateby more than a predetermined amount (e.g. few millimeters), a levelbelow an empirical value of the hard palate (e.g., 5 mm below theempirical value) is selected as the separation level. In the particularembodiment described, the determined hard palate is used to divide orseparate the anatomic features of interest from those portions of thehead that are not of interest (step 106). Since most dental features arelocated below the hard palate, the output of this portion of the processis the dataset representing the image data below the separation level(or hard palate).

Once detection of the hard palate has occurred and the anatomy ofinterest has been separated from other anatomy (using the hard palate asa dividing line) (step 106), automatic seeding is performed as part ofthe overall process of separating the jaw (or bone) from soft tissue(step 108). (As should be understood, although the expression“separating bone from tissue” (or similar expressions) is (are) used,the image data is being separated or categorized as either data thatrepresents bone or data that represents tissue.) The seeding isperformed in a manner that accounts, at least in part, for challengesassociated with distinguishing bone from tissue due to voids or gapsthat may be present in or around the teeth. In addition, the seedingprocess also helps account for other areas of low density that may existinside the mandible and maxilla (e.g., pockets of air or liquid).

To achieve segmentation where all or nearly all of the hard tissues oranatomical parts are separated from soft tissues (or parts), amulti-thread seeding technique is employed (step 110). The multi-threadseeding technique involves applying a two-dimensional grid net over theupper portion of each image slice. If an image point that overlaps withthe grid net has an intensity higher than a predetermined amount (e.g.,900), the image point is selected as (or assumed to be) a bone point andput into the set of seeding points. The bone point selection process isperformed from the top left to right across the image and moves frompoint to point with a predetermined gap (e.g., 3 mm). In one particularimplementation, the total number of selected points is limited to apredetermined amount (e.g., 20).

Once bone points are selected, they are processed staring from thebottom slice that contains at least one bone point (i.e., a point withan intensity of a predetermined amount) and going up slice by slice(once a single bone point is found). If the last selected point on thecurrent slice is far away from the last selected point (e.g., more thanfew millimeters) on the previous slice, then the selected points beforethe current slice are discarded (i.e., removed from the set of bonepoints) and the process restarts from the current slice. In thisprocess, if no bone points are present in the current slice, the nextslice is analyzed. Each image slice is processed until a predeterminedheight (e.g., a height no higher than the separation level (or hardpalate)) is reached. After all the slices have been processed in onedirection, the process is then reversed and performed in the oppositedirection. Bone points detected or determined in the reverse directionare added to the set of seeding points. The number of the selectedpoints for this downward or reverse process is also limited to apredetermined amount (e.g., 20). The total number of seeding points(i.e., bone points) is limited (e.g., 40) and forms an ordered set. Tohelp achieve fast computation, an order of operations is obeyed in theregion growing process (e.g., last in points are processed first).

Once the seeding is complete, a region growing segmentation process oralgorithm is employed (step 110). The segmentation process determineswhether the current point is a point on the jaw bone. If the point beingevaluated (i.e., the current point) satisfies certain conditions, thepoint is classified as one to be included in the segmented volume.

After this classification, neighboring points on the same slice (e.g., 8points (front, back, left, right, front-left, front-right, back-left,back-right)) and the two points above and below the current point areanalyzed. The analysis performed is referred to as a “region growingprocess.” Points within this set of 10 points that satisfy certainconditions are classified as seeding points. These conditions includemodified intensity, standard deviation, and anatomical locations. Theanalysis of neighboring points is repeated for all of the seedingpoints.

As the process of classifying points in the segmented volume (i.e., thedata representing the volume below the separation level or hard palate)occurs, the set of seeding points dynamically changes. In other words,the points in the set change during each cycle of the process becausethe points already classified in the segmented volume are not put intothe set of seeding points again.

As should be apparent from the description above, the process of growingseeding points is a last-in, first-out process. Also, it is alocal-nested process that fits the region with holes and gaps withmultiple threads. The seeding process helps ensure that all isolatedportions or bony portions with holes are evaluated or judged and, asappropriate, classified as bone or hard tissue.

The result or output of the multi-thread seeding and region growingprocess is a set of binary images. In one embodiment, “1s” in each imageindicate voxels on the jaw and “0s” in the image indicate voxels off (oroutside of) the jaw. As is described below in greater detail, the binaryimage data is processed further and ultimately used to produce apanoramic image. However, and as an alternative, the binary images canbe used to render a three-dimensional image of the jaw separated fromsoft tissue (step 112) instead of or in addition to a panoramic image.

Once the binary images are produced, the binary image data is separatedinto slices, (step 120 of FIG. 7). This process involves putting thethree-dimensionally segmented binary image data into a stack of binary,two-dimensional image slices. Performing this process helps speedcomputations that are performed in certain downstream steps.

After separating the binary image data into slices, an outer archdetection process is performed (step 124). In particular, the locationof the outer arch in the segmented-volume, binary data is detected ordetermined. Outer arch detection is achieved by locating the envelope ofthe jaw in a two-dimensional slice from the outside of the jaw arch.

In one embodiment, two distance metrics are employed for outer archboundary identification. One metric is the left distance from theleft-most edge of the image to the nearest horizontal jaw bone, and theother metric is the right distance from the right-most edge of the imageto the nearest horizontal jaw bone. Two parallel and combined proceduresare performed for the left-half of the jaw arch and the right-half ofthe jaw arch. For the left-half of the jaw arch, the points with thelocal minimum distances and the points with smaller left distances thanthose immediately prior are considered boundary points. A similarprocess is applied to the right half of the jaw. The collection or setof these boundary points (i.e., the points from the left half and righthalf of the jaw) constitutes the outer boundary of the jaw arch. Toachieve a better estimate of the jaw arch, a curvature-basedinterpolation of the set of boundary points is performed. More pointsare interpolated in regions with a high or relatively high curvature.The detected outer boundary points are ordered from small to large oftheir corresponding radial angles related to the left horizontal segmentfrom the center of the image.

In addition to determining the location of the outer arch, the locationof the inner arch is determined (step 128). In one embodiment, the innerarch boundary is detected by determining a fitting curve of the innerjaw arch in each two-dimensional slice from the inside of the jaw arch.In one embodiment, two distance metrics are employed for inner archboundary identification. One metric is the left distance from a middlevertical line of the image to the nearest horizontal jaw bone, and theother metric is the right distance from a middle vertical line of theimage to the nearest horizontal jaw bone. Two parallel and combinedprocedures are performed for the left-half of the jaw arch and theright-half of the jaw arch.

For the left-half of the jaw arch, the points with the local minimumdistances and the points with smaller left distances than those pointsimmediately prior are considered boundary points (referred to as the“inner arch boundary point rule.”) A similar process is performed for(or boundary point rule is applied to) the right-half of the jaw arch.However, the inner arch boundary rule is valid only if the jaw has oneconcave shape at the very front region of the jaw. If the inner shape ofthe front jaw includes two concaves, the inner boundary points for eachsuch concave are collected. Determining whether one or two concavesexist is accomplished by checking the voxels in the middle region of theline (or curve) for their locations and intensity. The collection of theabove boundary points constitutes the inner boundary of the jaw arch.

As was done with the outer arch, to achieve a better estimate of theinner jaw arch, a curvature-based interpolation of the set of boundarypoints is performed. More points are interpolated in regions with a highor relatively high curvature. The detected outer boundary points areordered from small to large of their corresponding radial angles relatedto the left horizontal segment from the center of the image.

As shown in step 132 of FIG. 7, the inner and outer boundaries of thejaw arch (or inner and outer arches) are processed so to produce anouter curve, inner curve, and central curve for each slice. The innerand outer boundaries are smoothed in three-dimensional shells formed bythe boundaries themselves. The process helps eliminate scenediscontinuity artifacts in the panoramic image (that is ultimatelyproduced).

Boundary smoothing is performed in three-dimensions instead of intwo-dimensions. A three-dimensional, spatial, lower-pass averagingfilter is employed for the curve processing to obtain new points forboth the inner boundary and the outer boundary. After smoothing of theinner and outer boundaries, the central curve of the jaw arch (normallycalled the location of the jaw arch) is obtained by averaging the innerboundary points and the outer boundary points with the same anglesrelative to the left horizontal segment from the center of the image.Points in pairs of points from the inner boundary and the outer boundaryare arranged in a one-to-one ratio.

The smoothing process can also be referred to or characterized as acurve-fitting process, since the purpose is to make the inner and outerlayers smooth enough to be seen as a natural surface of a real object,even though the curve is formed artificially based on the detected outerand inner curves.

As shown in step 136 in FIG. 8, when the outer and inner arches havebeen detected and boundary smoothing performed, a master arch detectionprocess is performed. One goal of the master arch detection process isto find an arch (central curve) on an axial slice which has theappearance of a solid shape. Preferably, the selected arch resembles asolid shape more than other axial arches in the slice of image data.Through observation and empirical investigation, it was determined thatthe slice containing a predetermined anatomical feature is the slicefrom which to determine the master arch. In particular, it has beendetermined that the slice containing the front portion of thecement-enamel junction (“CEJ”) curve is the best slice from which toselect the master arch. The master arch is roughly the longest curve inthe slice and, generally, has relatively solid inner and outerboundaries that represent the typical shape of the jaw.

Determining the vertical level of the master arch from the chosen sliceis accomplished by analyzing a local maximum protrusion of the frontteeth. To find the local maximum front protrusion, a loop search of thefront teeth data is performed to find the vertical level with thehighest or largest level. If the vertical level of the master arch(i.e., the maximum protrusion) cannot be found using this procedure, a“golden” division (i.e., based on golden arithmetic) between theseparation slice and the bottom slice containing the jaw bottom isperformed to obtain the master arch level. Empirical testing hasdemonstrated that golden division works well for a database of more than500 datasets.

After it is detected, the master arch is processed by rearranging thepoints on the master arch (step 140) so that it can be used for auniformly-spaced projection (step 144). The projection is from thesurface spanned by the master arch to all the central curves (i.e., thecentral curve in each slice).

Prior to making the projections, in step 140, the two tails of themaster arch are processed so that they are positioned towards (oraligned with) the left and right boundaries of the master arch. The twotails are also extended until they touch the image boundaries. Theextension helps ensure that the complete anatomies of all the othercurves' tails (bounded by the master arch) are included (in the endimage), because the curves in the teeth region may be longer than thenon-extended master arch. The points on the master arch are counted tocompute the length of the master arch. New evenly-spaced points are thengenerated and substituted for the previously existing points of themaster arch. The number of new generated points for the master arch isequal to the length of the panoramic image to be produced. The distancebetween all of the neighboring points is one voxel wide.

Once the tails of the master arch are adjusted, orthogonal projectionsfrom the master arch are made (step 144). One projection is made to eachcentral curve and the intersection points of the projected lines withthe central curve are recorded or stored. If a projection does not crossa point on the central curve, the left closest point and the rightclosest point on the central curve are employed to create a point on thecentral curve. The relative distance of the left closest point to theprojection and the relative distance of the right closest point to theprojection are weighted in creation of a new point on the central curve.As a consequence, the number of the intersection points is the same asthe number of points in the master arch. As noted above, this number isthe length of the panoramic image to be reconstructed. The number ofslices is the width of the panoramic image to be reconstructed.

The central curves are rearranged or reorganized using the new pointsgenerated through projection (step 148). The inner and outer arches, thecentral curves and the master arch are all used to create panoramicimages (step 152). Two types of panoramic images can be produced at theuser's discretion (e.g., based on user input indicating a desire for thesystem 10 to generate one type of image versus the other or both). Thedefault one (referred to as a “radiograph”) is similar to a conventionalpanoramic image. The other is referred to as a maximum intensityprojection (“MIP”) panoramic image.

Generating the radiograph involves finding the local normal vector ateach point of the central curve. The direction of the normal vector isfrom the inside of the central curve to the outside of the centralcurve. Ray summation is performed along the normal vector from theinside to the outside to generate the default panoramic image. In oneembodiment, the default path is 14 mm in length and with the point ofthe central curve as the center. Based on empirical evidence, this 14 mmthickness is a typical thickness of jaw arches. However, the thicknesscan be changed if the user desires.

For the MIP panoramic image, the maximum intensity is taken for theintensity of the panoramic image along the same path specified for theray summation. For each point on the central curve, one pixel isidentified and each central curve represents one horizontal line on thepanoramic image. The slices form the entire panoramic image. However,the central curve on the separation slice is employed for all the slicesabove the separation level. The jaw anatomy above the separation levelis nested with fosse and other tissue. Segmentation and inclusion ofthis bone information would create visual artifacts and hide thevisualization of the sinus floor and mandibular condyles. Excluding thisbone information increases the clarity of the sinus floor and mandibularcondyles and yields a panoramic image that is similar or better thanconventional radiographs.

Since the separation level (as well as the separation slice) is close tothe teeth apices, the teeth apices and sinus floor region are pronouncedand the continuity is naturally extended. Also, since the separationslice is far above the traditional focal trough, the comprehensiveemployment of the local bony information of the jaw below the separationslice makes the entire jaw more pronounced than traditional panoramicimages and conventional radiographs.

Thus, the invention provides, among other things, an x-ray imagingsystem that generates improved panoramic images. Various features andadvantages of the invention are set forth in the following claims.

What is claimed is:
 1. A method of generating an image, the methodcomprising: obtaining, with an x-ray detector, image data having a firstplurality of slices; defining a vertical threshold for the image data;separating, with a computer, the image data into a first portion abovethe vertical threshold and a second portion below the verticalthreshold; processing, with the computer, the second portion to generatean arch; and generating, with the computer, an image based on the arch.2. The method as claimed in claim 1, wherein processing, with thecomputer, the second portion to generate an arch includes separating,with the computer, the second portion into a second plurality of slices;and generating, with the computer, the arch for the second plurality ofslices
 3. The method as claimed in claim 1, further comprisingsegmenting the second portion of the image data to separate image datathat represents bone from image data that represents tissue.
 4. Themethod as claimed in claim 3, further comprising performing a seedingprocess on an upper portion of each of the second plurality of slices.5. The method as claimed in claim 4, wherein the seeding processincludes a multi-thread seeding technique involving applying atwo-dimensional grid net over the upper portion of each of the secondplurality of slices.
 6. The method as claimed in claim 1, whereingenerating an arch for the plurality of slices includes generating aplurality of curves for each slice in the second plurality of slices. 7.The method as claimed in claim 6, wherein generating an arch for theplurality or slices includes determining a slice from the secondplurality of slices that includes a predetermined anatomical feature anddetermining the longest curve in the slice.
 8. The method as claimed inclaim 1, wherein generating a plurality of curves for each slice in thesecond plurality of slices includes generating an outer curve, an innercurve, and a central curve.
 9. The method as claimed in claim 1, furthercomprising making a plurality of projections from the arch.
 10. Themethod as claimed in claim 1, further comprising; selecting a sagittalslice from the image data; and iteratively checking image element valuesin the sagittal slice.
 11. An x-ray system comprising: an x-ray source;an x-ray detector positioned opposite the x-ray source; and a computerthat receives image data from the x-ray detector, the computer defininga vertical threshold for the image data; separating the image data intoa first portion above the vertical threshold and a second portion belowthe vertical threshold; separating the second portion of data into aplurality of slices; generating an arch for the plurality of slices; andgenerating an image based on the arch.
 12. The system as claimed inclaim 11, wherein the computer segments the second portion of the imagedata to separate image data that represents bone from image data thatrepresents tissue.
 13. The system as claimed in claim 12, wherein thecomputer performs a seeding process, over an upper portion of eachslice.
 14. The system as claimed in claim 13, wherein the seedingprocess includes a multi-thread seeding technique involving applying atwo-dimensional grid net over the upper portion of each slice.
 15. Thesystem as claimed in claim 11, wherein generating an arch for theplurality of slices includes generating a plurality of curves for eachslice of the plurality of slices, determining a slice from the pluralityof slices that includes a predetermined anatomical feature, anddetermining the longest curve in the slice.
 16. The system as claimed inclaim 11, wherein generating a plurality of curves for each sliceincludes generating an outer curve, an inner curve, and a central curve.17. The system as claimed in claim 11, wherein the computer generates aplurality of projections from the arch.
 18. A method of generating animage, the method comprising: obtaining, with an x-ray detector, imagedata including a plurality of slices, each slice having a plurality ofvoxel values; selecting, with a computer, a predetermined slice from theimage data; iteratively checking, with the computer, voxel values in thepredetermined slice; seeding, with the computer, each slice in theplurality of slices; generating, with the computer, a set of imagesbased on region growing; and generating a three-dimensional image basedon the set of images.